1. Field of the Invention
The present invention relates to the field of characterizing the properties of a field, particularly to the characterization of properties within a field relating to mass transfer or material movement. The characterization of field properties is related to fields such as localized absorption/adsorption of materials to surfaces or volumes, especially in the delivery of bioactive materials to in vivo tissue during therapeutic treatments.
2. Background of the Art
It is a routine procedure in image processing to “segment” a two-dimensional (2D) or three-dimensional (3D) image. This can be performed, for example, on 1) a photograph of a slice of biological material, or 2) a scan by computer-aided tomography (CAT), magnetic resonance imaging (MRI), sonogram, or seismology recordation systems, where the data resembles a stack of slices. Many ways of gathering such data exist in various areas of technology, such as, for example, scanning data from individual components in an image to provide distinct layers with related data (e.g., only single colors, only ranges of optical density, etc.).
The image provided in such segmenting comprises a rectangular array of points with specified intensity, density or color values, the points being called “pixels” in 2D and “voxels” in 3D. The objective of the segmentation procedure is to classify these points into classes of materials so that information can be read into or from the image. In a specialized technological field, such as in medical applications, for example, “bone”, “muscle”, “blood”, or “tumor” may be assigned to different points and ultimately areas of the image. In geology, the classes might be “granite”, “shale”, “water”, etc., and so on for other sciences. The typical starting point for classification of the image is the value attached to a specific point. For example, bone absorbs X-rays more than any other tissue, so that one can fix a threshold value T according to the scaling of a particular Computed Axial Tomography (CAT) image and use “opacity greater than I” as a test for attaching the label “bone” to a point. A “segment” is then either a) the class of points so classified, or b) a connected component of this class. For example, it can be useful in medical applications to recognize the left kidney and the right kidney as separate segments of the body. Various methods also exist for correcting errors and noise in the image. For example, an isolated voxel with a value associated with muscle, surrounded by voxels classifiable as bone, should usually be labeled as bone also.
There exists also a large range of software to assist humans in segmentation, allowing them to draw curves around a region so that the regions can be recognized as separate. For example, where a region presses closely against another region with similarly associated values (such as a bone touching a bone), single values alone would be inadequate for classifying points. Despite such software assistance, this region recognition technology is labor intensive, especially in 3D where the classification is usually done slice by slice. Therefore, improved automation is a continuing area of vigorous research. We assume herein that it is desirable that segmentation includes not only a labeling of grid points by segments, but also that some descriptive geometric information such as bounding areas such as its bounding box (the smallest rectangular region with sides parallel to the x, y and z axes that contains every point of it) or a list of boxes in some scheme such as an octree division of the grid is provided, so that between all of these components of the segmentation labeling system, all of the points of the segment are included and identified (classified). Such additional data allow iteration over a shorter sequence of grid points than the whole set, when points outside the segment will contribute nothing to the intended result of the classification within the segmentation. Where such data are not delivered with the segmentation, they can be rapidly constructed for the entire segment list by a single run-through of the grid. Therefore, we assume that they are available.
Once 3D segmentation is achieved, it allows volumetry, the defining and/or measuring of the volume of a segment, which is proportional to the number of voxels labeled as belonging to it. This can be important either in static images, such as a geological or body scan where it answers questions like “How much ore is present?” or “is the liver enlarged?” or in a sequence of 3D images, such as a record of a beating heart, where the change in volume of the left ventricle can assist in quantifying what fraction of the blood is expelled into the aorta, and thus how effectively the heart is functioning as a pump. As a point is either In or Out of a segment (it is or is not blood, muscle, granite, etc.), volumetry techniques have developed for this two-valued context. The present invention addresses the computation of quantities appropriate to, and by methods appropriate to, continuous-valued variables such as a density or concentration.